Modeling and Predicting Students’ Academic Performance Using Data Mining Techniques

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Author(s)

Ahmed Mueen 1,* Bassam Zafar 1 Umar Manzoor 1

1. King Abdulaziz University, Saudi Arabia, Jeddah

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2016.11.05

Received: 6 Aug. 2016 / Revised: 16 Sep. 2016 / Accepted: 2 Oct. 2016 / Published: 8 Nov. 2016

Index Terms

Educational Data Mining, Classification, Academic performance prediction, Knowledge Discovery

Abstract

The main objective of this study is to apply data mining techniques to predict and analyze students' academic performance based on their academic record and forum participation. Educational Data Mining (EDM) is an emerging tool for academic intervention. The educational institutions can use EDU for extensive analysis of students’ characteristics. In this study, we have collected students’ data from two undergraduate courses. Three different data mining classification algorithms (Naïve Bayes, Neural Network, and Decision Tree) were used on the dataset. The prediction performance of three classifiers are measured and compared. It was observed that Naïve Bayes classifier outperforms other two classifiers by achieving overall prediction accuracy of 86%. This study will help teachers to improve student academic performance.

Cite This Paper

Ahmed Mueen, Bassam Zafar, Umar Manzoor, "Modeling and Predicting Students' Academic Performance Using Data Mining Techniques", International Journal of Modern Education and Computer Science(IJMECS), Vol.8, No.11, pp.36-42, 2016. DOI:10.5815/ijmecs.2016.11.05

Reference

[1]Alkhasawneh and R. Hobson, “Modeling student retention in science and engineering disciplines using neural networks,” in Proc. IEEE Global Eng. Edu. Conf. (EDUCON), pp. 660–663, Apr. 2011.
[2]Araque, F., Roldan, C., & Salguero, A. “Factors influencing university dropout rates,” Journal of Computer & Education, 53, pp.563–574, 2009.
[3]Attaway, N. M., & Bry, B. H. “Parenting style and black adolescents’ academic achievement,” Journal of Black Psychology, 30, pp.229–247, 2004.
[4]Baker, R.S., Corbett, A.T., Koedinger, K.R. “Detecting Student Misuse of Intelligent Tutoring Systems,” Proceedings of the 7th International Conference on Intelligent Tutoring Systems, pp.531-540, 2004.
[5]Baker, R.S.J.d. ”Data Mining for Education”. In McGaw, B., Peterson, P., Baker, E. (Eds.) International Encyclopedia of Education (3rd edition), vol. 7, pp. 112-118, 2010.
[6]Bassam Zafar, Ahmed Mueen, Mohammad Awedh, Mohammad Balubaid, “Game-based learning with native language hint and their effects on student academic performance in a Saudi Arabia community college” Computer in Education vol. 1, no. 4, pp. 371-384, 2014.
[7]Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. “Classification and Regression Trees,” Belmont, CA: Wadsworth International Group 1984.
[8]Calvo-Flores, M. D., Galindo, E. G., Jimenez, M. P., & Pineiro, O. P. “Predicting students’ marks from Moodle logs using neural network models,” Current Developments in Technology-Assisted Education, 1, pp. 586–590, 2006.
[9]Charu, C. A. “An Introduction to Data Classification” Data Classification, Chapman and Hall/CRC, pp. 1-36, 2014.
[10]Cristóbal Romero, Manuel-Ignacio López, Jose-María Luna, Sebastián Ventura. “Predicting students' final performance from participation in on-line discussion forums”, Journal of computer and Education vol. 68, pp.458-472, 2013.
[11]Essa, A., & Ayad, H. “Student success system: Risk an alytics and data visualization using ensembles of predictive models,” Paper presented at the 2nd international conference on learning analytics and knowledge, Vancouver 2012.
[12]Fayyad.U, Piatesky, G. Shapiro, and P. Smyth, “From data mining to knowledge discovery in databases,” AAAI Press , Massachusetts Institute Of Technology The MIT Press 1996,
[13]Gerber, S. B., & Fin, J. D. “Teacher aides and students” academic achievement Educational Evaluation and Policy Analysis, 23(2), pp.123–143, 2001.
[14]Goddard, R. D., Sweetland, S. R., & Hoy, W. K. “Academic emphasis of urban elementary schools and student achievement in reading and mathematics: A multilevel analysis,” Educational Administration Quarterly, 36(5), pp.683–702, 2000,
[15]Hand D.j., & Yu, K. “Idiot’s Bayes- not so stupid after all?” International Statistical Review, 69(3), pp.385- 399, 2001.
[16]Hornik, K., Stinchcombe, M., & White, H. (1990). “Universal approximation of an unknown mapping and its derivatives using multilayer feedforward network,”Neural Networks, 3, pp.359–366, 1990.
[17]Hongbo, D., Yizhou, S., Yi, C., & Jiawei, H. “Probabilistic Models for Classification,” Data Classification, Chapman and Hall/CRC, (pp. 65-86), 2014.
[18]Kotsiantis, S. B. “Use of machine learning techniques for educational proposes: A decision support system for forecasting students grades,” Artificial Intelligence Review, 37(4), pp.331–344, 2012,.
[19]Mostow, J., Beck, J., Cuneo, A., Gouvea, E., & Heiner, C. “A Generic Tool to Browse Tutor-Student Interactions: Time Will Tell,” Proceedings of the 12th International Conference on Artificial Intelligence in Education (AIED 2005), pp. 884-886, 2005 Amsterdam,.
[20]Quinlan, J. R. “C4.5: programs for machine Learning,” Morgan Kaufmann Publishers Inc, 1993.
[21]Romero, C., & Ventura, S.“Data mining in Education,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), pp.12–27, 2013.
[22]Tang, T., McCalla, G. “Smart recommendation for an evolving e-learning system: architecture and experiment”, International Journal on E-Learning, vol. 4, issue1, pp.105–129, 2005.
[23]U Manzoor, S Nefti "An agent based system for activity monitoring on network-ABSAMN" Expert Systems with Applications 36 (8), pp. 10987-10994, 2009.
[24]Weka, (2015). Retrieved from http://www.cs.waikato.ac.nz/ml/weka/.
[25][25]Xindong Wu, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J. McLachlan, Angus F. M. Ng, Bing Liu, Philip S. Yu, Zhi-Hua Zhou, Michael Steinbach, David J. Hand, Dan Steinberg “Top 10 algorithms in data mining,” Knowl. Inf. Syst. 14(1), pp.1-37, 2008.
[26]Zaïane, O. “Building a recommender agent for e- learning systems”. Proceedings of the International Conference on Computers in Education, pp.55–59, 2002.